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使用tensorflow用对象检测代码无法看到结果

python小能手 2019-02-28 11:49:50 648

我正在使用开放代码进行对象检测,使用GitHub提供的张量流。我能够毫无错误地运行代码。

但是,在完成代码运行后,我无法看到任何图像。通常,一旦代码运行,测试图像将显示在检测到的图像周围的边界框以及检测到的对象的名称。我甚至没有得到最后的图像。但我可以在文件夹中看到测试图像。请帮我。我是python的新手并测试代码。

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  • uncle_ll
    2019-08-13 12:39:04

    看代码 是否被注释掉了, 如果没有被注释掉,自己写一段就行,使用opencv挺容易实现的

    0 0
  • 卡诺门窗
    2019-07-23 15:12:05

    我也遇到这样的问题

    0 0
  • python小能手
    2019-07-17 23:29:41

    我更喜欢使用cv2的图像,你可以将此代码放在research / object_detection路径为 detect_object.py

    文件必须放在这里: model-master/research/object_detection/detect_object.py

    import numpy as np
    import os
    import six.moves.urllib as urllib
    import sys
    import tarfile
    import tensorflow as tf
    import zipfile

    from distutils.version import StrictVersion
    from collections import defaultdict
    from io import StringIO
    import cv2

    This is needed since the notebook is stored in the object_detection folder.

    sys.path.append("..")
    from object_detection.utils import ops as utils_ops

    if StrictVersion(tf.__version__) < StrictVersion('1.9.0'):
    raise ImportError('Please upgrade your TensorFlow installation to v1.9.* or later!')

    Object detection imports

    Here are the imports from the object detection module.

    from utils import label_map_util

    from utils import visualization_utils as vis_util

    What model to download.

    MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'
    MODEL_FILE = MODEL_NAME + '.tar.gz'
    DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'

    Path to frozen detection graph. This is the actual model that is used for the object detection.

    PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'

    List of the strings that is used to add correct label for each box.

    PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')

    opener = urllib.request.URLopener()

    opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)

    tar_file = tarfile.open(MODEL_FILE)
    for file in tar_file.getmembers():
    file_name = os.path.basename(file.name)
    if 'frozen_inference_graph.pb' in file_name:

    tar_file.extract(file, os.getcwd())
    

    detection_graph = tf.Graph()
    with detection_graph.as_default():
    od_graph_def = tf.GraphDef()
    with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:

    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')
    

    In[31]:

    category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)

    Size, in inches, of the output images.

    IMAGE_SIZE = (12, 8)

    def run_inference_for_single_image(image, graph):
    with graph.as_default():

    with tf.Session() as sess:
      # Get handles to input and output tensors
      ops = tf.get_default_graph().get_operations()
      all_tensor_names = {output.name for op in ops for output in op.outputs}
      tensor_dict = {}
      for key in [
          'num_detections', 'detection_boxes', 'detection_scores',
          'detection_classes', 'detection_masks'
      ]:
        tensor_name = key + ':0'
        if tensor_name in all_tensor_names:
          tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
              tensor_name)
      if 'detection_masks' in tensor_dict:
        # The following processing is only for single image
        detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
        detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
        # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
        real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
        detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
        detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
        detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
            detection_masks, detection_boxes, image.shape[0], image.shape[1])
        detection_masks_reframed = tf.cast(
            tf.greater(detection_masks_reframed, 0.5), tf.uint8)
        # Follow the convention by adding back the batch dimension
        tensor_dict['detection_masks'] = tf.expand_dims(
            detection_masks_reframed, 0)
      image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
    
      # Run inference
      output_dict = sess.run(tensor_dict,
                             feed_dict={image_tensor: np.expand_dims(image, 0)})
    
      # all outputs are float32 numpy arrays, so convert types as appropriate
      output_dict['num_detections'] = int(output_dict['num_detections'][0])
      output_dict['detection_classes'] = output_dict[
          'detection_classes'][0].astype(np.uint8)
      output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
      output_dict['detection_scores'] = output_dict['detection_scores'][0]
      if 'detection_masks' in output_dict:
        output_dict['detection_masks'] = output_dict['detection_masks'][0]

    return output_dict

    image = cv2.imread("test_images/image1.jpg")

    the array based representation of the image will be used later in order to prepare the

    result image with boxes and labels on it.

    image_np = load_image_into_numpy_array(image)

    Expand dimensions since the model expects images to have shape: [1, None, None, 3]

    image_np_expanded = np.expand_dims(image, axis=0)

    Actual detection.

    output_dict = run_inference_for_single_image(image, detection_graph)

    vis_util.visualize_boxes_and_labels_on_image_array(

    image,
    output_dict['detection_boxes'],
    output_dict['detection_classes'],
    output_dict['detection_scores'],
    category_index,
    instance_masks=output_dict.get('detection_masks'),
    use_normalized_coordinates=True,
    line_thickness=8)

    cv2.imshow("",image)

    0 0
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